Recent News

Keunwoo Choi, George Fazekas, Mark Sandler and I have received the Best Paper Award at the 18th Annual International Society for Music Information Retrieval Conference (ISMIR). The paper is <Transfer Learning for Music Classification and Regression Tasks> which investigates different ways to exploit the knowledge captured within a deep convolutional network trained to tag a song for other relevant tasks. The main idea is to use not only the final hidden activation vector (as has been usual in computer vision) but to use the activations from all the layers, as some target tasks may require low-level details. Check it out at https://arxiv.org/abs/1703.09179 and https://github.com/keunwoochoi/transfer_learning_music.

This work started while Keunwoo was visiting me at the NYU Center for Data Science. And, of course, he's done all the work from the beginning to the end. Congratulations and thank you, Keunwoo!

I'm happy to announce that I've been selected as a CIFAR Azrieli Global Scholar this year (announcement). This appointment is for two years, and I will be able to attend awesome CIFAR meetings to hang out with and hear from awesome CIFAR fellows. I am especially looking forward to meetings organized by the Learning in Brain and Machine (LMB) Programme of CIFAR co-directed by Yoshua Bengion and Yann LeCun, in addition to interacting with other Azrieli Global Scholars and a broader set of fellows from other programmes at CIFAR . It is my understanding that this is not intended to award my previous research but to support my future research, and I will use this opportunity to my best to keep up my research.

My research proposal on <A Trainable Decoding Algorithm for Neural Machine Translation> has been selected for Google Research Award 2016 (it's a bit confusing whether it's 2016 or 2017; deadline in 2016 but decision in 2017.) I'd like to thank Google for this award which would greatly help my research. Gotta go buy a few more GPU's!

Applicants are expected to have strong background and experience in developing and investigating deep neural networks for computer vision, in addition to good knowledge of machine learning and excellent programming skills. Applicants should be able to implement deep neural networks, including multilayered convolutional networks and recurrent networks, for a large-scale data which consists of many high-resolution images and associated textual descriptions.

The appointment will be for one year, with the option of renewing for a further year, dependent on satisfactory performance. The candidate will be expected to interact with other students and faculties in CILVR.

To be considered for the position, send your CV, list of publications and the contact details of two references to kyunghyun.cho@nyu.edu.

I have been awarded Google Faculty Award (Fall 2015) in the field of machine translation. I am honoured to be a recipient of this award and will use it toward advancing my machine translation research further.

One major issue with research in Q&A is that there is not a controlled but large-scale standard benchmark dataset available. There are a number of open-ended Q&A datasets, but they often require a system to have access to external resources. This makes it difficult for researchers to compare different models in a unified way.

Recently, one such large-scale standard Q&A dataset was proposed by Hermann et al. (2015). In this dataset, a question comes along with a context of which one of the word is an answer to the question. And.. wait.. I just realized that I don't have to explain the dataset nor the task here at all. After all, it's not my data/task nor my paper. I will just leave a link to the original paper:

So, what is an issue with this dataset? It's that the dataset was not published online. I can understand why, even without asking them (though, I neither confirm nor deny any interaction between me and DeepMind or anyone there,) and you can probably guess as well (though, among the two you guessed, a less evil one it probably is.) They instead released a script to generate the dataset, and I am grateful for their effort.

This is unfortunately never fun to spend a few hours generating a dataset, isn't it? Nothing to worry about your laziness anymore! Because, I generated the dataset and am making it available for you to download at

The Center for Data Science (CDS) at NYU has a weekly lunch seminar series. Each Monday, one speaker gives an (informal) presentation on any topic she/he wants to talk about, or at least so I thought. Anyways, I thought it would be a good chance to discuss with people (students, research fellows at CDS as well as faculty members from various departments all over NYU) what the interpretability of machine learning models means. I prepared a set of slides based on an excellent article <Statistical Modeling: The Two Cultures> by Leo Breiman.

Instead of trying to write what I've talked about here, I'll put a link to my slides:

I was invited to give lectures on natural language processing with deep learning at the DENIS Summer School held in Espoo, Finland this year. It was really good to be back in Finland after 1.5 years (or more like 2 years, since I was travelling France and Italy half of the month I stayed there 1.5 years ago.) The weather was amazing, the sauna was pretty cool (shame I didn't have enough time to enjoy it fully!) and it was great to meet the friends and former colleagues there.

The Summer School itself was also great with some awesome talks by Razvan Pascanu at Google DeepMind and Tapani Raiko at Aalto University, and with enthusiastic audience. Razvan talked to us about Theano and reinforcement learning, and Tapani about his latest work on semi-supervised learning with a ladder network (congrats to the authors of this paper on NIPS acceptance!)